An integrated multispectral face recognition system

Face recognition at a distance is one grand challenge for security surveillance. In this paper, the face images at different distances are simulated by varying image scales (resolutions). The performances of three face recognition algorithms (matchers) are tested with variant image scales (simulating different distances) and with two spectral images (modalities). The three selected matchers are face pattern byte, elastic bunch graph matching, and linear discriminant analysis; while the two modalities are visible and thermal images. The performance of a face recognition system can be measured by accuracy (AC) rate and false accept rate (FAR). To enhance the performance of face recognition especially at a distance, score fusion techniques are applied, which combine several scores from multiple matchers and multiple modalities. Our experiments are conducted with the ASUMS face dataset consisting of two spectral images (visible and thermal) from 135 subjects. The experimental results show that the face recognition with small image scales (simulating long distances) have low performance (e.g., AC=91.36%, FAR=8.64% for 20×20-pixel images); and score fusion can greatly improve accuracy (99.34%) meanwhile reduce FAR (0.31%).

There are strong evidences of that multimodal biometric score fusion can significantly improve human identification performance. Score level fusion usually involves score normalization, score fusion, and fusion decision. There are several types of score fusion methods, direct combination of fusion scores, classifier-based fusion, and density-based fusion. The real applications require achieving greater reliability in determining or verifying person's identity. The goal of this research is to improve the accuracy and robustness of human identification by using multimodal biometrics score fusion. The accuracy means high verification rate if tested on a closed dataset, or a high genuine accept rate under low false accept rate if tested on an open dataset. While the robustness means the fusion performance is stable with variant biometric scores. We propose a hidden Markov model (HMM) for multiple score fusion, where the biometric scores include multimodal scores and multi-matcher scores. The state probability density functions in a HHM model are estimated by Gaussian mixture model. The proposed HMM model for multiple score fusion is accurate for identification, flexible and reliable with biometrics. The proposed HMM method are tested on three NIST-BSSR1 multimodal databases and on three face-score databases. The results show the HMM method is an excellent and reliable score fusion method.

In recent times, there have been increased rates at which researchers are searching for advanced ways of carrying out land-use land-cover (LULC) mapping, especially in developing countries. Four machine-learning algorithms, namely Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Gaussian Mixture Models (GMM) were examined. This study also attempted to validate the various models using the index-based validation method. Accuracy assessment was performed by using the Kappa coefficient. The results of the LULC showed that RF classified 23% of the study area as bare land, SVM has 24% of the study area classified as bare land, K-NN also allotted 24% to bare land, while that of GMM classifier was 30%. The overall accuracy of RF, SVM, K-NN and GMM were 0.9840, 0.9780, 0.9641 and 0.9421 respectively. The Kappa Coefficient of the various classifiers were RF (0.9695), SVM (0.9580), K-NN (0.9319) and GMM (0.8916). This study showed that though all the algorithms performed relatively very well, RF performed better than the other classifiers. It suffices to state that, there is a need for further studies since other extraneous environmental variables may be underpinning these conclusions.

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.

Condition monitoring techniques are described in this chapter. Two aspects of condition monitoring process are considered: (1) feature extraction; and (2) condition classification. Feature extraction methods described and implemented are fractals, kurtosis, and Mel-frequency cepstral coefficients. Classification methods described and implemented are support vector machines (SVM), hidden Markov models (HMM), Gaussian mixture models (GMM), and extension neural networks (ENN). The effectiveness of these features was tested using SVM, HMM, GMM, and ENN on condition monitoring of bearings and are found to give good results.

K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are the two Supervised Machine Learning Algorithms which have been used extensively in solving classification and regression problems in various application domains—medical diagnosis, pattern recognition, and image classification being the popular ones. KNN has been attractive for its simplicity—SVM has been considered as complex. Computational complexity of KNN is less compared to SVM. However, accuracy of SVM in general has been higher compared to KNN. This paper carries out an analysis of complexity of KNN and SVM and then looks at the alternative strategies to reduce the complexity. Accuracies of both SVM and KNN are analyzed from literature survey of experimental results with different training datasets. Reduction of number of training instances and usage of kernel functions are found to be the solutions for reducing SVM complexity. Factors impacting KNN accuracy are analyzed and possible alternatives are assessed. Kernel functions and distance-weighted KNN are concluded as the solution to improve KNN accuracy to compete with SVM accuracy. To utilize the best potential of the two algorithms, the option of a hybrid algorithm combining both KNN and SVM is also proposed.

Spoken language identification is a field of research that is already being done by many people. There are many techniques proposed for doing speech processing, such as Support Vector Machines, Gaussian Mixture Models, Decision Trees, and others. This paper will use the system using the Mel-Frequency Cepstral Coefficient (MFCC) features of speech input signal, use Random Forest (RF), Gaussian Mixture Model (GMM), and K-Nearest Neighbor (KNN) as a classifier, use the 3s, 10s, and 30s as scoring method, and use dataset that consists of Javanese, Sundanese, and Minang languages which are traditional languages from Indonesia. K-Nearest Neighbor has 98.88% of accuracy for 30s of speech and followed by Random Forest that has 95.55% of accuracy for 30s of speech, GMM has 82.24% of accuracy.

Speech Emotion Recognition (SER) plays a vital role in various real-world applications, from mental health diagnostics to human-computer interaction. This research examines the integration of Stacked Long Short-Term Memory (LSTM) networks and Hidden Markov Models (HMM) for SER, addressing the limitations of traditional models like Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). While GMMs and SVMs offer decent performance, their inability to model temporal dynamics limits their accuracy in detecting complex emotions. The proposed hybrid model leverages LSTM’s strength in handling sequential data and HMM’s ability to model emotional transitions, making it highly suitable for real-world noisy environments. Preprocessing techniques such as MFCC and LPCC are applied to enhance feature extraction, and benchmark datasets like IEMOCAP and RAVDESS are used for evaluation. Finally, The Systematic review highlights the superior role of the hybrid model's performance on SER and sets the stage for a significant shift in future research in addressing bias and fairness in SER systems by combining LSTM and HMM as hybrid model. In recent studies, context-aware emotion recognition models are commonly evaluated using existing datasets such as IEMOCAP, MELD, DailyDialog, and SEMAINE, which provide conversational and multimodal emotional data. Features used often include acoustic features as MFCCs, spectrograms, prosodic features, linguistic features as BERT or transformer embeddings, and visual cues such as facial landmarks or video frames. Many models also incorporate contextual embeddings from transformers, graph attention networks, or sequential memory modules to capture speaker history and emotional shifts.

A hybrid model comprised of Gaussian Mixtures Models (GMMs) and Hidden Markov Models (HMMs) is used to model generic sounds with large intra class perceptual variations. Each class has variable number of mixture components in the GMM. The number of mixture components is derived using the Minimum Description Length (MDL) criterion. The overall performance of the hybrid model was compared against models based on HMMs and GMMs with a fixed number of mixture components across all classes. We show that a hybrid model outperforms both class-based GMMs, HMMs, and GMMs based on fixed number of components. Further, our experiments revealed that the contribution of transitions between states in HMMs has no significant effect on the overall classification performance of generic sounds when large intra class perceptual variations are present among sounds in the training and test datasets. Sounds that show multi-event structure with events that tend to be similar (repetitive) indicated improved performance when modeled with HMMs that can be attributed to HMM’s state transition property. Conversely, GMMs indicate better performance when the sound samples show subtle or no repetitive behavior. These results were validated using the MuscleFish sound database.

In this work, our objective is to study the impact of statistical hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60, 90 and 120 meters. Next, the selected statistical analysis test is used to find the statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the statistical analysis reveals which descriptors result in higher recognition performance. Finally, statistical hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of testing FR accuracy only on the female Asian class) when employing database stratification.

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

Content-based audio classification and segmentation is a basis for further audio/video analysis. In this paper, we present our work on audio segmentation and classification which employs support vector machines (SVMs). Five audio classes are considered in this paper: silence, music, background sound, pure speech, and non- pure speech which includes speech over music and speech over noise. A sound stream is segmented by classifying each sub-segment into one of these five classes. We have evaluated the performance of SVM on different audio type-pairs classification with testing unit of different- length and compared the performance of SVM, K-Nearest Neighbor (KNN), and Gaussian Mixture Model (GMM). We also evaluated the effectiveness of some new proposed features. Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation. It also shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.

In this work, our objective is to study the impact of statistical hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60, 90 and 120 meters. Next, the selected statistical analysis test is used to find the statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the statistical analysis reveals which descriptors result in higher recognition performance. Finally, statistical hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of testing FR accuracy only on the female Asian class) when employing database stratification.

With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition. In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential features: (1) word formation pattern; (2) morphological pattern, such as prefix and suffix; (3) part-of-speech; (4) head noun trigger; (5) special verb trigger and (6) name alias feature. All the features are integrated effectively and efficiently through a hidden Markov model (HMM) and a HMM-based named entity recognizer. In addition, a k-Nearest Neighbor (k-NN) algorithm is proposed to resolve the data sparseness problem in our system. Finally, we present a pattern-based post-processing to automatically extract rules from the training data to deal with the cascaded entity name phenomenon. From our best knowledge, PowerBioNE is the first system which deals with the cascaded entity name phenomenon. Evaluation shows that our system achieves the F-measure of 66.6 and 62.2 on the 23 classes of GENIA V3.0 and V1.1, respectively. In particular, our system achieves the F-measure of 75.8 on the "protein" class of GENIA V3.0. For comparison, our system outperforms the best published result by 7.8 on GENIA V1.1, without help of any dictionaries. It also shows that our HMM and the k-NN algorithm outperform other models, such as back-off HMM, linear interpolated HMM, support vector machines, C4.5, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem. Moreover, evaluation on GENIA V3.0 shows that the post-processing for the cascaded entity name phenomenon improves the F-measure by 3.9. Finally, error analysis shows that about half of the errors are caused by the strict annotation scheme and the annotation inconsistency in the GENIA corpus. This suggests that our system achieves an acceptable F-measure of 83.6 on the 23 classes of GENIA V3.0 and in particular 86.2 on the "protein" class, without help of any dictionaries. We think that a F-measure of 90 on the 23 classes of GENIA V3.0 and in particular 92 on the "protein" class, can be achieved through refining of the annotation scheme in the GENIA corpus, such as flexible annotation scheme and annotation consistency, and inclusion of a reasonable biomedical dictionary. A demo system is available at http://textmining.i2r.a-star.edu.sg/NLS/demo.htm. Technology license is available upon the bilateral agreement.

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.